AI infrastructure FinOps and procurement
Stop Overpaying for AI Infrastructure
Reduce model API, cloud AI, and GPU costs without compromising product quality, latency, reliability, or compliance.
- Unified cost ledger across APIs, clouds, and GPU capacity
- Contract-aware recommendations tied to margin impact
- Approved routing, caching, and procurement workflows
For AI companies spending more than $50,000 per month across model APIs, cloud AI services, or GPU infrastructure.
- Route summarization traffic to a lower-cost model Ready to implement
- Enable prompt caching for support-agent workloads Under evaluation
- Move batch processing to reserved GPU capacity Requires approval
- Reduce unused Azure provisioned throughput Completed
Optimize your entire AI stack
Plug seamlessly into your existing stack with no provider replacement, application rewrite, or forced traffic migration.
The problem
AI Spending Is Growing Faster Than Your Ability to Control It
Engineering sees token usage. Finance sees invoices. Procurement sees contracts. Product teams see customer behavior. No one sees the complete relationship between infrastructure cost, product performance, customer revenue, and business outcomes.
Traditional cloud FinOps tools were built for servers, databases, and storage. AI infrastructure introduces tokens, context length, reasoning usage, cache behavior, model routing, GPU utilization, evaluation quality, latency, and provider-specific pricing.
Main value
Know What Every AI Request Costs and Whether It Is Worth It
Connect AI spending to the product, feature, agent, customer, and business outcome that generated it.
Platform
One Control Plane for AI Infrastructure Economics
Normalize spending, attribute costs to the business, evaluate alternatives, and execute approved optimization policies from one operating layer.
Normalize every cost driver into one ledger
Model APIs, cloud AI, GPU hours, credits, commitments, cached tokens, and negotiated pricing land in one finance-ready view.
Expose margin by customer and product surface
See which workspaces, agents, workflows, and customer segments create healthy margin or need policy changes.
Compare providers using cost, latency, and quality together
Recommendations are tested against real workloads, not generic benchmarks or list-price assumptions.
Route work by financial and operational policy
Use premium models only when quality, plan tier, region, and contract economics justify the spend.
- Reserved capacity before pay-as-you-go
- Batch workloads to the lowest-cost approved provider
- Human approval for high-cost reasoning models
Reduce waste from prompts, context, and agent loops
Detect repeated system prompts, duplicate retrieval, low-value reasoning calls, poor cache use, and runaway retries.
Right-size self-hosted and private inference
Monitor utilization, idle endpoints, queue depth, autoscaling behavior, and cost per successful output.
Turn renewals and commitments into managed workflows
Track pricing, discounts, prepaid credits, renewals, overage terms, SLAs, rate limits, and underuse risk.
Outcomes
Turn AI Infrastructure Into a Managed Business Function
Reduce Model Costs
Continuously identify lower-cost models and providers that satisfy your requirements.
Improve Gross Margin
Measure AI cost by customer, plan, feature, and business outcome.
Control Commitments
Track minimums, credits, renewals, reserved throughput, and pricing terms.
Prevent Cost Incidents
Detect runaway agents, usage spikes, routing failures, and context growth early.
Automate Optimization
Update routing policies, budgets, caching rules, and infrastructure configs after approval.
Procurement agent
An AI Procurement Agent for Contract Decisions
Continuously monitor invoices, reconcile usage, prepare provider comparisons, forecast spend, and create negotiation briefs before renewals arrive.
We expect model usage to grow 70% over six months. Should we commit to Azure provisioned throughput, purchase AWS Bedrock capacity, or stay usage-based?
Commit 55% of baseline traffic to Azure provisioned capacity, keep 25% usage-based for variability, and maintain 20% on AWS Bedrock for failover.
How it works
Start Saving in Weeks, Not Quarters
Connect Your AI Infrastructure
Connect billing, usage, telemetry, contract data, observability, warehouses, and product analytics.
Build Your AI Cost Map
Map spending to models, providers, products, customers, teams, workflows, and outcomes.
Validate Opportunities
Evaluate output quality, latency, reliability, safety, compliance, engineering effort, and financial impact.
Approve and Implement
Create tickets, update routing policies, enable caching, adjust model selection, and reallocate traffic.
Verify Savings
Track actual results against an agreed baseline adjusted for growth, product changes, and seasonality.
Free savings audit
Begin With a Free AI Infrastructure Savings Audit
The initial review identifies the most valuable cost-reduction opportunities across your current AI spend. If we cannot identify meaningful, actionable savings, you pay nothing.
Book Your Free Savings AuditWe analyze
- Provider invoices and pricing agreements
- Token consumption and model usage
- GPU utilization and endpoint uptime
- Reserved capacity and commitments
- Caching, routing, anomalies, and high-cost workflows
You receive
- Complete AI spend map
- Cost by provider, model, customer, and feature
- Top optimization opportunities
- Contract and commitment risks
- 90-day roadmap and annual savings estimate
Example results
What a Typical Optimization Could Look Like
Before Optimization
$350,000 monthly AI infrastructure spend- Premium model overuse: $72,000
- Low cache utilization: $38,000
- Unprofitable customer workloads: $24,000
- Idle GPU capacity: $18,000
With Automated Optimization
$274,000 monthly AI infrastructure spendResults vary based on workload, infrastructure, contracts, usage volume, and implementation decisions.
Interactive ROI
Estimate Savings Before the Audit Call
Adjust monthly AI spend and provider concentration to model a conservative savings range. The audit validates the real number against your contracts, workloads, and quality requirements.
Use cases
Built for Companies Where AI Cost Directly Affects Margin
AI Agent Companies
Control tool calls, reasoning loops, retries, and customer-level usage.
Coding Tools
Measure cost per completion, repository analysis, debugging task, and active customer.
Customer Support AI
Optimize cost per ticket while maintaining resolution quality and response latency.
AI API Platforms
Track margin across customers, models, providers, and usage tiers.
Generative Media
Control image, audio, and video generation costs across providers and product plans.
Private Deployments
Compare hosted APIs against self-hosted models and optimize GPU capacity.
Why AI needs different FinOps
Why Traditional FinOps Fails for AI Workloads
Security
Your Data Stays Under Your Control
Built for companies operating sensitive AI products and infrastructure. Prompt content is not required for basic cost analysis, and customers configure the telemetry and metadata shared with the platform.
Pricing
Pricing Aligned With the Value We Create
Pricing combines a predictable platform fee with an optional performance fee based on verified savings. There are no hidden infrastructure markups and no requirement to purchase model capacity through the platform.
FAQ
Questions AI infrastructure leaders usually ask first
Does this replace our existing AI gateway?
No. It can integrate with your existing gateway, observability platform, or infrastructure.
Do we need to move all model traffic?
No. The initial analysis can use billing, usage, telemetry, and contract data without moving production traffic.
Will cost optimization reduce quality?
Recommendations are evaluated against your quality, latency, reliability, compliance, and safety requirements.
How are savings calculated?
We establish a baseline using historical usage, pricing, volume, and workload data, then adjust for traffic and product changes.
Can it manage negotiated provider pricing?
Yes. The cost ledger uses your actual contract pricing, credits, commitments, and discount structures.
Does it store prompts?
Prompt content is not required for basic cost monitoring. Advanced evaluation can use metadata, redacted traces, or customer-controlled datasets.
Take control
Your AI Infrastructure Bill Should Not Be a Black Box
See exactly where your AI budget is going, which workloads are driving cost, and what actions can reduce spending without damaging your product.